Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. U.S.A. 106, 9362-9367

Office of Population Genomics, Genome Technology Branch, National Human Genome Research Institute, and National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20892, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 07/2009; 106(23):9362-7. DOI: 10.1073/pnas.0903103106
Source: PubMed


We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36%, interquartile range (IQR) 21%-53%] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20-1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2-7.0), p = 3.5 x 10(-7)] and 5kb-promoter regions [OR = 2.3 (1.5-3.6), p = 3 x 10(-4)] compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34-0.58), p = 2.0 x 10(-9)]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8-2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.

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Available from: Teri Manolio, Jan 12, 2014
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    • "Deciphering the genetic and molecular basis of human traits and disease is a fundamental problem in biology and personalized medicine. Genome-wide association studies (GWAS) and deep sequencing efforts have identified common and rare single nucleotide genetic variants, as well as structural variants associated with diseases ranging from inflammatory bowel disease and Alzheimer's disease to cancer (Hindorff et al., 2009; Lambert et al., 2013; Rivas et al., 2011; Zuk et al., 2014). A majority of diseaseassociated common variants lie in poorly annotated non-coding genomic regions. "
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    ABSTRACT: Deciphering the impact of genetic variants on gene regulation is fundamental to understanding human disease. Although gene regulation often involves long-range interactions, it is unknown to what extent non-coding genetic variants influence distal molecular phenotypes. Here, we integrate chromatin profiling for three histone marks in lymphoblastoid cell lines (LCLs) from 75 sequenced individuals with LCL-specific Hi-C and ChIA-PET-based chromatin contact maps to uncover one of the largest collections of local and distal histone quantitative trait loci (hQTLs). Distal QTLs are enriched within topologically associated domains and exhibit largely concordant variation of chromatin state coordinated by proximal and distal non-coding genetic variants. Histone QTLs are enriched for common variants associated with autoimmune diseases and enable identification of putative target genes of disease-associated variants from genome-wide association studies. These analyses provide insights into how genetic variation can affect human disease phenotypes by coordinated changes in chromatin at interacting regulatory elements. Copyright © 2015 Elsevier Inc. All rights reserved.
    Cell 08/2015; 162(5). DOI:10.1016/j.cell.2015.07.048 · 32.24 Impact Factor
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    • "noncoding and are thought to act by affecting TF binding sites (Hindorff et al. 2009; Maurano et al. 2012). The SNP probes of our arrays will be particularly valuable in evaluating whether a disease-associated or traitassociated SNP alters the binding of specific TFs. "
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    ABSTRACT: To achieve proper spatiotemporal control of gene expression, transcription factors cooperatively assemble onto specific DNA sequences. The ETS domain protein monomer of GABPα and the B-ZIP domain protein dimer of CREB1 cooperatively bind DNA only when the ETS ((C)/GCGGAA GT: ) and CRE ( GT: GACGTCAC) motifs overlap precisely, producing the ETS⇔CRE motif ((C)/GCGGAA GT: GACGTCAC). We designed a Protein Binding Microarray (PBM) with 60-bp DNAs containing four identical sectors, each with 177,440 features that explore the cooperative interactions between GABPα and CREB1 upon binding the ETS⇔CRE motif. The DNA sequences include all 15-mers of the form (C)/GCGGA-----CG---, the ETS⇔CRE motif and all single nucleotide polymorphisms (SNPs), and occurrences in the human and mouse genomes. CREB1 enhanced GABPα binding to the canonical ETS⇔CRE motif CCGGAAGT 2-fold, and up to 23-fold for several SNPs at the beginning and end of the ETS motif, which is suggestive of two separate and distinct allosteric mechanisms of cooperative binding. We show that the ETS-CRE array data can be used to identify regions likely cooperatively bound by GABPα and CREB1 in vivo, and demonstrate their ability to identify human genetic variants that might inhibit cooperative binding. Copyright © 2015 Author et al.
    G3-Genes Genomes Genetics 07/2015; 5(9). DOI:10.1534/g3.115.020248 · 3.20 Impact Factor
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    • "In the past decade, genome-wide association studies (GWAS) have been conducted to study the genetic basis for thousands of phenotypes (Hindorff et al., 2009; Eicher et al., 2015), including diseases (e.g., the seven diseases from WTCCC, The Wellcome Trust Case Control Consortium, 2007), clinical traits (e.g., cholesterol levels), anthropometric traits (e.g., height, Wood et al., 2014), brain structures (Hibar et al., 2015) and social behaviors (e.g., educational attainment, Rietveld et al., 2013; marriage, Domingue et al., 2014). As of April, 2015, more than 15,000 single-nucleotide polymorphisms (SNPs) have been reported to be significantly associated (p < 5 × 10 −8 ) with at least one phenotype (see GWAS catalog, Welter et al., 2014). "
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    ABSTRACT: Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation). The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment.
    Frontiers in Genetics 06/2015; 6:229. DOI:10.3389/fgene.2015.00229
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